Inclusive Environmental Intelligence in the 6G Era: Architectural Principles and Edge Learning for Ecological Sensing

Résumé fourni par la source

Rural and remote regions worldwide often face limited connectivity, deepening the digital divide. As the first mobile generation designed to bridge this gap, 6G envisions ubiquitous, intelligent, and sustainable connectivity. This paper presents a 6G-native architecture that integrates non-terrestrial networks, reconfigurable intelligent surfaces, and edge artificial intelligence for scalable ecological sensing. We propose a multi-tier edge architecture—spanning edge, fog, and cloud layers—to support energy efficiency, resilience, and distributed intelligence. Key Edge AI learning paradigms, including federated, continual, reinforcement, and split learning, are explored to enable privacy-preserving and adaptive intelligence across dynamic, bandwidth-constrained environments. The framework is aligned with 6G's vision of ubiquitous coverage and context-aware decision-making. Finally, we highlight research challenges in connectivity, learning robustness, hardware constraints, security, interoperability, and sustainability. This work positions 6G-driven systems as a catalyst for ecological intelligence and digital equity.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.